## [1] "2024-04-12 13:33:13 CEST"
## [1] "explicated variable of regression : rh98"
## [1] "for  all_Africa_regression_rh98.RDS"                       
## [2] "for  Guinean_forest-savanna_regression_rh98.RDS"           
## [3] "for  Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [4] "for  Sahelian_Acacia_savanna_regression_rh98.RDS"          
## [5] "for  Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [6] "for  West_Sudanian_savanna_regression_rh98.RDS"            
## [7] "for  Western_Congolian_forest-savanna_regression_rh98.RDS" 
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "below, stancode for all_Africa_regression_rh98.RDS"
## // generated with brms 2.20.4
## functions {
## }
## data {
##   int<lower=1> N;  // total number of observations
##   vector[N] Y;  // response variable
##   int<lower=1> K;  // number of population-level effects
##   matrix[N, K] X;  // population-level design matrix
##   int<lower=1> Kc;  // number of population-level effects after centering
##   int prior_only;  // should the likelihood be ignored?
## }
## transformed data {
##   matrix[N, Kc] Xc;  // centered version of X without an intercept
##   vector[Kc] means_X;  // column means of X before centering
##   for (i in 2:K) {
##     means_X[i - 1] = mean(X[, i]);
##     Xc[, i - 1] = X[, i] - means_X[i - 1];
##   }
## }
## parameters {
##   vector[Kc] b;  // regression coefficients
##   real Intercept;  // temporary intercept for centered predictors
##   real<lower=0> shape;  // shape parameter
## }
## transformed parameters {
##   real lprior = 0;  // prior contributions to the log posterior
##   lprior += student_t_lpdf(Intercept | 3, 1.3, 2.5);
##   lprior += gamma_lpdf(shape | 0.01, 0.01);
## }
## model {
##   // likelihood including constants
##   if (!prior_only) {
##     // initialize linear predictor term
##     vector[N] mu = rep_vector(0.0, N);
##     mu += Intercept + Xc * b;
##     mu = exp(mu);
##     target += gamma_lpdf(Y | shape, shape ./ mu);
##   }
##   // priors including constants
##   target += lprior;
## }
## generated quantities {
##   // actual population-level intercept
##   real b_Intercept = Intercept - dot_product(means_X, b);
## }
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] " "
## [1] " "
## [1] "all_Africa_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 13362) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           1.51      0.00     1.50     1.52 1.00     3070     3255
## fire_freq_std       0.08      0.00     0.07     0.09 1.00     3275     3106
## mean_precip_std     0.31      0.00     0.30     0.32 1.00     3091     2629
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     4.84      0.06     4.73     4.95 1.00     3182     2831
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Guinean_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 1725) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           1.96      0.03     1.91     2.02 1.00     3075     3028
## fire_freq_std       0.03      0.01     0.01     0.05 1.00     3072     3045
## mean_precip_std     0.06      0.01     0.04     0.09 1.00     3046     3023
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     3.80      0.12     3.56     4.04 1.00     3076     3171
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 243) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           2.13      0.19     1.76     2.49 1.00     3075     3089
## fire_freq_std      -0.03      0.03    -0.09     0.04 1.00     3042     3003
## mean_precip_std    -0.00      0.09    -0.17     0.17 1.00     3065     3131
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     5.01      0.45     4.15     5.91 1.00     3021     3166
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Sahelian_Acacia_savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 5563) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           1.32      0.01     1.31     1.33 1.00     3297     3124
## fire_freq_std       0.11      0.01     0.10     0.12 1.00     3220     3216
## mean_precip_std     0.33      0.01     0.31     0.35 1.00     3299     3134
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape    18.01      0.34    17.34    18.69 1.00     3452     3136
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 47) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           3.37      0.65     2.10     4.68 1.00     3137     3092
## fire_freq_std      -0.08      0.04    -0.16     0.01 1.00     2968     3183
## mean_precip_std    -0.96      0.31    -1.57    -0.36 1.00     3195     3092
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     6.07      1.25     3.88     8.75 1.00     3131     2904
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "West_Sudanian_savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 3277) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           1.22      0.02     1.19     1.25 1.00     3067     3114
## fire_freq_std       0.08      0.01     0.07     0.10 1.00     3044     3056
## mean_precip_std     0.53      0.02     0.50     0.56 1.00     3052     2874
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     5.47      0.13     5.21     5.73 1.00     3600     2875
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
##  Family: gamma 
##   Links: mu = log; shape = identity 
## Formula: rh98 ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 259) 
##   Draws: 4 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 3200
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           1.36      0.16     1.05     1.66 1.00     2982     3193
## fire_freq_std      -0.05      0.02    -0.10    -0.00 1.00     2648     2861
## mean_precip_std     0.36      0.10     0.18     0.56 1.00     3026     3172
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape     3.07      0.26     2.61     3.60 1.00     3508     3132
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "explicated variable of regression : canopy_cover"
## [1] "for  all_Africa_regression_canopy_cover.RDS"                       
## [2] "for  Guinean_forest-savanna_regression_canopy_cover.RDS"           
## [3] "for  Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "for  Sahelian_Acacia_savanna_regression_canopy_cover.RDS"          
## [5] "for  Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "for  West_Sudanian_savanna_regression_canopy_cover.RDS"            
## [7] "for  Western_Congolian_forest-savanna_regression_canopy_cover.RDS" 
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "below, stancode for all_Africa_regression_canopy_cover.RDS"
## // generated with brms 2.20.4
## functions {
##   /* zero-inflated beta log-PDF of a single response
##    * Args:
##    *   y: the response value
##    *   mu: mean parameter of the beta distribution
##    *   phi: precision parameter of the beta distribution
##    *   zi: zero-inflation probability
##    * Returns:
##    *   a scalar to be added to the log posterior
##    */
##    real zero_inflated_beta_lpdf(real y, real mu, real phi, real zi) {
##      row_vector[2] shape = [mu * phi, (1 - mu) * phi];
##      if (y == 0) {
##        return bernoulli_lpmf(1 | zi);
##      } else {
##        return bernoulli_lpmf(0 | zi) +
##               beta_lpdf(y | shape[1], shape[2]);
##      }
##    }
##   /* zero-inflated beta log-PDF of a single response
##    * logit parameterization of the zero-inflation part
##    * Args:
##    *   y: the response value
##    *   mu: mean parameter of the beta distribution
##    *   phi: precision parameter of the beta distribution
##    *   zi: linear predictor for zero-inflation part
##    * Returns:
##    *   a scalar to be added to the log posterior
##    */
##    real zero_inflated_beta_logit_lpdf(real y, real mu, real phi, real zi) {
##      row_vector[2] shape = [mu * phi, (1 - mu) * phi];
##      if (y == 0) {
##        return bernoulli_logit_lpmf(1 | zi);
##      } else {
##        return bernoulli_logit_lpmf(0 | zi) +
##               beta_lpdf(y | shape[1], shape[2]);
##      }
##    }
##   // zero-inflated beta log-CCDF and log-CDF functions
##   real zero_inflated_beta_lccdf(real y, real mu, real phi, real zi) {
##     row_vector[2] shape = [mu * phi, (1 - mu) * phi];
##     return bernoulli_lpmf(0 | zi) + beta_lccdf(y | shape[1], shape[2]);
##   }
##   real zero_inflated_beta_lcdf(real y, real mu, real phi, real zi) {
##     return log1m_exp(zero_inflated_beta_lccdf(y | mu, phi, zi));
##   }
## }
## data {
##   int<lower=1> N;  // total number of observations
##   vector[N] Y;  // response variable
##   int<lower=1> K;  // number of population-level effects
##   matrix[N, K] X;  // population-level design matrix
##   int<lower=1> Kc;  // number of population-level effects after centering
##   int prior_only;  // should the likelihood be ignored?
## }
## transformed data {
##   matrix[N, Kc] Xc;  // centered version of X without an intercept
##   vector[Kc] means_X;  // column means of X before centering
##   for (i in 2:K) {
##     means_X[i - 1] = mean(X[, i]);
##     Xc[, i - 1] = X[, i] - means_X[i - 1];
##   }
## }
## parameters {
##   vector[Kc] b;  // regression coefficients
##   real Intercept;  // temporary intercept for centered predictors
##   real<lower=0> phi;  // precision parameter
##   real<lower=0,upper=1> zi;  // zero-inflation probability
## }
## transformed parameters {
##   real lprior = 0;  // prior contributions to the log posterior
##   lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
##   lprior += gamma_lpdf(phi | 0.01, 0.01);
##   lprior += beta_lpdf(zi | 1, 1);
## }
## model {
##   // likelihood including constants
##   if (!prior_only) {
##     // initialize linear predictor term
##     vector[N] mu = rep_vector(0.0, N);
##     mu += Intercept + Xc * b;
##     mu = inv_logit(mu);
##     for (n in 1:N) {
##       target += zero_inflated_beta_lpdf(Y[n] | mu[n], phi, zi);
##     }
##   }
##   // priors including constants
##   target += lprior;
## }
## generated quantities {
##   // actual population-level intercept
##   real b_Intercept = Intercept - dot_product(means_X, b);
## }
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] " "
## [1] " "
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 13362) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -2.79      0.02    -2.82    -2.76 1.00     2504     2181
## fire_freq_std       0.09      0.01     0.07     0.10 1.00     2397     2356
## mean_precip_std     0.42      0.01     0.40     0.44 1.00     2355     2287
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     7.13      0.12     6.90     7.38 1.00     2428     1976
## zi      0.34      0.00     0.34     0.35 1.00     2320     2366
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 1725) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.95      0.06    -2.06    -1.84 1.00     2329     2393
## fire_freq_std      -0.03      0.02    -0.07     0.02 1.00     2512     2410
## mean_precip_std     0.12      0.03     0.07     0.17 1.00     2246     2185
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     5.28      0.19     4.93     5.68 1.00     2300     2519
## zi      0.11      0.01     0.10     0.13 1.00     2349     2292
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 243) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.77      0.36    -2.49    -1.06 1.00     2062     2093
## fire_freq_std      -0.00      0.06    -0.13     0.11 1.00     2320     2300
## mean_precip_std     0.10      0.17    -0.24     0.41 1.00     2021     2090
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     6.72      0.62     5.57     7.98 1.00     2172     2174
## zi      0.06      0.02     0.03     0.09 1.00     2462     2175
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 5563) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -3.89      0.04    -3.96    -3.81 1.00     2460     2389
## fire_freq_std       0.18      0.03     0.12     0.23 1.00     2266     2215
## mean_precip_std     0.85      0.06     0.73     0.97 1.00     2353     2288
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi    27.54      1.24    25.14    29.96 1.00     2436     2332
## zi      0.68      0.01     0.67     0.69 1.00     2484     2090
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 47) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.68      2.43    -6.50     3.17 1.00     2565     2352
## fire_freq_std      -0.04      0.15    -0.34     0.25 1.00     2875     2352
## mean_precip_std    -0.20      1.18    -2.55     2.05 1.00     2539     2409
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     4.47      1.51     2.01     7.78 1.00     2263     2328
## zi      0.51      0.07     0.38     0.64 1.00     2388     2116
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 3277) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -3.52      0.05    -3.61    -3.42 1.00     2378     2285
## fire_freq_std       0.08      0.01     0.05     0.11 1.00     2402     2369
## mean_precip_std     0.90      0.04     0.82     0.98 1.00     2507     2256
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     9.97      0.32     9.36    10.63 1.00     2315     2251
## zi      0.23      0.01     0.21     0.24 1.00     2245     2288
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
##  Family: zero_inflated_beta 
##   Links: mu = logit; phi = identity; zi = identity 
## Formula: canopy_cover ~ fire_freq_std + mean_precip_std 
##    Data: table_region (Number of observations: 259) 
##   Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
##          total post-warmup draws = 2400
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept          -1.91      0.33    -2.54    -1.25 1.00     2474     2412
## fire_freq_std      -0.13      0.05    -0.23    -0.03 1.00     2465     2251
## mean_precip_std     0.20      0.21    -0.22     0.58 1.00     2423     2348
## 
## Family Specific Parameters: 
##     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi     3.51      0.39     2.79     4.30 1.00     2551     2455
## zi      0.22      0.03     0.17     0.28 1.00     2147     2145
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] "########################################"
## [1] "Gamma regressions for  all_Africa_regression_rh98.RDS"                       
## [2] "Gamma regressions for  Guinean_forest-savanna_regression_rh98.RDS"           
## [3] "Gamma regressions for  Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [4] "Gamma regressions for  Sahelian_Acacia_savanna_regression_rh98.RDS"          
## [5] "Gamma regressions for  Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [6] "Gamma regressions for  West_Sudanian_savanna_regression_rh98.RDS"            
## [7] "Gamma regressions for  Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "all_Africa_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 13362  3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 5.329668
## [1] "sd(table_region$rh98)"
## [1] 3.457679

## [1] "mean(simulations[,j]) ( truth = 5.33 )"
## [1] 5.467
## [1] "sd(simulations[,j]) ( truth = 3.458 )"
## [1] 3.534

## [1] "mean(simulations[,j]) ( truth = 5.33 )"
## [1] 5.409
## [1] "sd(simulations[,j]) ( truth = 3.458 )"
## [1] 3.561

## [1] "mean(simulations[,j]) ( truth = 5.33 )"
## [1] 5.415
## [1] "sd(simulations[,j]) ( truth = 3.458 )"
## [1] 3.639

## [1] "mean(simulations[,j]) ( truth = 5.33 )"
## [1] 5.349
## [1] "sd(simulations[,j]) ( truth = 3.458 )"
## [1] 3.403

## [1] "mean(simulations[,j]) ( truth = 5.33 )"
## [1] 5.377
## [1] "sd(simulations[,j]) ( truth = 3.458 )"
## [1] 3.575

## [1] "Guinean_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 1725 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 8.17064
## [1] "sd(table_region$rh98)"
## [1] 4.228092

## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.186
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.181

## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.258
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.257

## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.152
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.26

## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.426
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.297

## [1] "mean(simulations[,j]) ( truth = 8.171 )"
## [1] 8.147
## [1] "sd(simulations[,j]) ( truth = 4.228 )"
## [1] 4.243

## [1] "Northern_Congolian_Forest-Savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  243 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 8.251706
## [1] "sd(table_region$rh98)"
## [1] 3.52155

## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 7.438
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.107

## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.468
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 4.046

## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.419
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.828

## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.102
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 3.218

## [1] "mean(simulations[,j]) ( truth = 8.252 )"
## [1] 8.299
## [1] "sd(simulations[,j]) ( truth = 3.522 )"
## [1] 4.057

## [1] "Sahelian_Acacia_savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 5563 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 3.049537
## [1] "sd(table_region$rh98)"
## [1] 1.187586

## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.064
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 1

## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.054
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.96

## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.052
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 1.002

## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.057
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.987

## [1] "mean(simulations[,j]) ( truth = 3.05 )"
## [1] 3.025
## [1] "sd(simulations[,j]) ( truth = 1.188 )"
## [1] 0.979

## [1] "Southern_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]   47 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 3.938866
## [1] "sd(table_region$rh98)"
## [1] 2.086246

## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 3.957
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.263

## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 4.164
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.032

## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 4.239
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.452

## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 3.611
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 1.731

## [1] "mean(simulations[,j]) ( truth = 3.939 )"
## [1] 3.837
## [1] "sd(simulations[,j]) ( truth = 2.086 )"
## [1] 2.04

## [1] "West_Sudanian_savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 3277 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 5.728088
## [1] "sd(table_region$rh98)"
## [1] 3.15554

## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.578
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.035

## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.674
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.056

## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.799
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.221

## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.736
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.131

## [1] "mean(simulations[,j]) ( truth = 5.728 )"
## [1] 5.768
## [1] "sd(simulations[,j]) ( truth = 3.156 )"
## [1] 3.151

## [1] "Western_Congolian_forest-savanna_regression_rh98.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  259 3200
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$rh98)"
## [1] 6.137749
## [1] "sd(table_region$rh98)"
## [1] 4.607916

## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.103
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.532

## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.179
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.789

## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 5.956
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.595

## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 6.776
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 4.458

## [1] "mean(simulations[,j]) ( truth = 6.138 )"
## [1] 5.819
## [1] "sd(simulations[,j]) ( truth = 4.608 )"
## [1] 3.187

## [1] "Beta regressions for  all_Africa_regression_canopy_cover.RDS"                       
## [2] "Beta regressions for  Guinean_forest-savanna_regression_canopy_cover.RDS"           
## [3] "Beta regressions for  Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "Beta regressions for  Sahelian_Acacia_savanna_regression_canopy_cover.RDS"          
## [5] "Beta regressions for  Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "Beta regressions for  West_Sudanian_savanna_regression_canopy_cover.RDS"            
## [7] "Beta regressions for  Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 13362  2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## Le chargement a nécessité le package : gtools
## 
## Attachement du package : 'gtools'
## Les objets suivants sont masqués depuis 'package:brms':
## 
##     ddirichlet, rdirichlet

## [1] "mean(table_region$canopy_cover)"
## [1] 0.05262057
## [1] "sd(table_region$canopy_cover)"
## [1] 0.09756511

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.047
## [1] "sd(simulations[,j]) ( truth = 0.098 )"
## [1] 0.084

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.046
## [1] "sd(simulations[,j]) ( truth = 0.098 )"
## [1] 0.084

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.047
## [1] "sd(simulations[,j]) ( truth = 0.098 )"
## [1] 0.087

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.047
## [1] "sd(simulations[,j]) ( truth = 0.098 )"
## [1] 0.086

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.048
## [1] "sd(simulations[,j]) ( truth = 0.098 )"
## [1] 0.086

## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 1725 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1314814
## [1] "sd(table_region$canopy_cover)"
## [1] 0.14466

## [1] "mean(simulations[,j]) ( truth = 0.131 )"
## [1] 0.127
## [1] "sd(simulations[,j]) ( truth = 0.145 )"
## [1] 0.143

## [1] "mean(simulations[,j]) ( truth = 0.131 )"
## [1] 0.128
## [1] "sd(simulations[,j]) ( truth = 0.145 )"
## [1] 0.138

## [1] "mean(simulations[,j]) ( truth = 0.131 )"
## [1] 0.128
## [1] "sd(simulations[,j]) ( truth = 0.145 )"
## [1] 0.137

## [1] "mean(simulations[,j]) ( truth = 0.131 )"
## [1] 0.137
## [1] "sd(simulations[,j]) ( truth = 0.145 )"
## [1] 0.144

## [1] "mean(simulations[,j]) ( truth = 0.131 )"
## [1] 0.132
## [1] "sd(simulations[,j]) ( truth = 0.145 )"
## [1] 0.141

## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  243 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1649144
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1334132

## [1] "mean(simulations[,j]) ( truth = 0.165 )"
## [1] 0.168
## [1] "sd(simulations[,j]) ( truth = 0.133 )"
## [1] 0.136

## [1] "mean(simulations[,j]) ( truth = 0.165 )"
## [1] 0.164
## [1] "sd(simulations[,j]) ( truth = 0.133 )"
## [1] 0.149

## [1] "mean(simulations[,j]) ( truth = 0.165 )"
## [1] 0.179
## [1] "sd(simulations[,j]) ( truth = 0.133 )"
## [1] 0.141

## [1] "mean(simulations[,j]) ( truth = 0.165 )"
## [1] 0.173
## [1] "sd(simulations[,j]) ( truth = 0.133 )"
## [1] 0.147

## [1] "mean(simulations[,j]) ( truth = 0.165 )"
## [1] 0.164
## [1] "sd(simulations[,j]) ( truth = 0.133 )"
## [1] 0.128

## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 5563 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.005161202
## [1] "sd(table_region$canopy_cover)"
## [1] 0.02202149

## [1] "mean(simulations[,j]) ( truth = 0.005 )"
## [1] 0.004
## [1] "sd(simulations[,j]) ( truth = 0.022 )"
## [1] 0.015

## [1] "mean(simulations[,j]) ( truth = 0.005 )"
## [1] 0.004
## [1] "sd(simulations[,j]) ( truth = 0.022 )"
## [1] 0.014

## [1] "mean(simulations[,j]) ( truth = 0.005 )"
## [1] 0.004
## [1] "sd(simulations[,j]) ( truth = 0.022 )"
## [1] 0.014

## [1] "mean(simulations[,j]) ( truth = 0.005 )"
## [1] 0.004
## [1] "sd(simulations[,j]) ( truth = 0.022 )"
## [1] 0.013

## [1] "mean(simulations[,j]) ( truth = 0.005 )"
## [1] 0.004
## [1] "sd(simulations[,j]) ( truth = 0.022 )"
## [1] 0.015

## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]   47 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.04984602
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1063901

## [1] "mean(simulations[,j]) ( truth = 0.05 )"
## [1] 0.052
## [1] "sd(simulations[,j]) ( truth = 0.106 )"
## [1] 0.114

## [1] "mean(simulations[,j]) ( truth = 0.05 )"
## [1] 0.059
## [1] "sd(simulations[,j]) ( truth = 0.106 )"
## [1] 0.101

## [1] "mean(simulations[,j]) ( truth = 0.05 )"
## [1] 0.073
## [1] "sd(simulations[,j]) ( truth = 0.106 )"
## [1] 0.11

## [1] "mean(simulations[,j]) ( truth = 0.05 )"
## [1] 0.046
## [1] "sd(simulations[,j]) ( truth = 0.106 )"
## [1] 0.085

## [1] "mean(simulations[,j]) ( truth = 0.05 )"
## [1] 0.025
## [1] "sd(simulations[,j]) ( truth = 0.106 )"
## [1] 0.048

## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 3277 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.05300694
## [1] "sd(table_region$canopy_cover)"
## [1] 0.08641946

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.053
## [1] "sd(simulations[,j]) ( truth = 0.086 )"
## [1] 0.077

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.054
## [1] "sd(simulations[,j]) ( truth = 0.086 )"
## [1] 0.082

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.051
## [1] "sd(simulations[,j]) ( truth = 0.086 )"
## [1] 0.075

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.052
## [1] "sd(simulations[,j]) ( truth = 0.086 )"
## [1] 0.078

## [1] "mean(simulations[,j]) ( truth = 0.053 )"
## [1] 0.053
## [1] "sd(simulations[,j]) ( truth = 0.086 )"
## [1] 0.081

## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1]  259 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"

## [1] "mean(table_region$canopy_cover)"
## [1] 0.1079882
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1649516

## [1] "mean(simulations[,j]) ( truth = 0.108 )"
## [1] 0.105
## [1] "sd(simulations[,j]) ( truth = 0.165 )"
## [1] 0.151

## [1] "mean(simulations[,j]) ( truth = 0.108 )"
## [1] 0.091
## [1] "sd(simulations[,j]) ( truth = 0.165 )"
## [1] 0.145

## [1] "mean(simulations[,j]) ( truth = 0.108 )"
## [1] 0.111
## [1] "sd(simulations[,j]) ( truth = 0.165 )"
## [1] 0.152

## [1] "mean(simulations[,j]) ( truth = 0.108 )"
## [1] 0.109
## [1] "sd(simulations[,j]) ( truth = 0.165 )"
## [1] 0.163

## [1] "mean(simulations[,j]) ( truth = 0.108 )"
## [1] 0.143
## [1] "sd(simulations[,j]) ( truth = 0.165 )"
## [1] 0.194